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IntroductionRare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer susceptibility for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD susceptibility, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability.MethodsIn a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability.ResultsBottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that susceptibility for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD.DiscussionOur findings suggest that cognitive ability itself may not be the factor driving the underlying liability for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits.

Background Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores. We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10−8, βrange = −1.46x10−6 to 9.59 × 10−7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level. These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.

Abstract Introduction Childhood trauma and adversity are common across societies and have strong associations with physical and psychiatric morbidity throughout the life-course. One possible mechanism through which childhood trauma may predispose individuals to poor psychiatric outcomes is via associations with brain structure. This study aimed to elucidate the associations between childhood trauma and brain structure across two large, independent community cohorts. Methods The two samples comprised (i) a subsample of Generation Scotland (n=1,024); and (ii) individuals from UK Biobank (n=27,202). This comprised n=28,226 for mega-analysis. MRI scans were processed using Free Surfer, providing cortical, subcortical, and global brain metrics. Regression models were used to determine associations between childhood trauma measures and brain metrics and psychiatric phenotypes. Results Childhood trauma associated with lifetime depression across cohorts (OR 1.06 GS, 1.23 UKB), and related to early onset and recurrent course within both samples. There was evidence for associations between childhood trauma and structural brain metrics. This included reduced global brain volume, and reduced cortical surface area with highest effects in the frontal (β=−0.0385, SE=0.0048, p(FDR)=5.43x10−15) and parietal lobes (β=−0.0387, SE=0.005, p(FDR)=1.56x10−14). At a regional level the ventral diencephalon (VDc) displayed significant associations with childhood trauma measures across both cohorts and at mega-analysis (β=−0.0232, SE=0.0039, p(FDR)=2.91x10−8). There were also associations with reduced hippocampus, thalamus, and nucleus accumbens volumes. Discussion Associations between childhood trauma and reduced global and regional brain volumes were found, across two independent UK cohorts, and at mega-analysis. This provides robust evidence for a lasting effect of childhood adversity on brain structure.

Objectives Generation Scotland (GS) is a family-based genetic epidemiology study of around 24,000 volunteers from 7000 families recruited across Scotland between 2006 and 2011 with follow-up through record linkage and re-contact. Broad consent was obtained for linkage to medical records for 98% of the cohort. Recruitment has recommenced in 2022. Approach Participants completed a demographic, health and lifestyle questionnaire, provided biological samples, and underwent detailed clinical assessment. The samples, phenotype and genotype data form a resource with broad consent for health-related research of current and projected public health importance. This has become a longitudinal dataset by linkage to routine NHS hospital, maternity, lab test, prescriptions, dentistry, mortality, cancer screening, GP data records, Covid-19 testing and vaccinations. Genome-wide association studies (GWAS) have been done on quantitative traits and biomarkers, with DNA methylation data and proteomics available for most of the cohort. Our “CovidLife” surveys collected data on the effects of the pandemic. Results Researchers can use the linked datasets to ascertain prevalent and incident disease cases and controls to test a wide range of research hypotheses. They can also recruit participants to new studies, including recall by genotype, utilising the NHS Scotland Community Health Index (CHI) Register for current contact details. We have established and validated electronic health record linkage, overcoming technical and governance issues in the process. Using consented data avoids some limitations of safe havens for analysis. GS is a contributor to major international consortia, with collaborators from many institutions worldwide, both academic and commercial. New recruits are now asked to give consent to linkage to other administrative data, and reuse of samples from routine NHS tests for medical research. Conclusion GS is now extending linkages to include radiology and health-relevant administrative data (e.g. education); and reopening recruitment to double the cohort size, including teenagers age 12+, collecting new data online and using remote sample collection. GS resources are available to academic and commercial researchers through a managed access process (www.generationscotland.org).

Abstract Background Recruitment is often a bottleneck in secondary prevention trials in Alzheimer disease (AD). Furthermore, screen-failure rates in these trials are typically high due to relatively low prevalence of AD pathology in individuals without dementia, especially among cognitively unimpaired. Prescreening on AD risk factors may facilitate recruitment, but the efficiency will depend on how these factors link to participation rates and AD pathology. We investigated whether common AD-related factors predict trial-ready cohort participation and amyloid status across different prescreen settings. Methods We monitored the prescreening in four cohorts linked to the European Prevention of Alzheimer Dementia (EPAD) Registry (n = 16,877; mean ± SD age = 64 ± 8 years). These included a clinical cohort, a research in-person cohort, a research online cohort, and a population-based cohort. Individuals were asked to participate in the EPAD longitudinal cohort study (EPAD-LCS), which serves as a trial-ready cohort for secondary prevention trials. Amyloid positivity was measured in cerebrospinal fluid as part of the EPAD-LCS assessment. We calculated participation rates and numbers needed to prescreen (NNPS) per participant that was amyloid-positive. We tested if age, sex, education level, APOE status, family history for dementia, memory complaints or memory scores, previously collected in these cohorts, could predict participation and amyloid status. Results A total of 2595 participants were contacted for participation in the EPAD-LCS. Participation rates varied by setting between 3 and 59%. The NNPS were 6.9 (clinical cohort), 7.5 (research in-person cohort), 8.4 (research online cohort), and 88.5 (population-based cohort). Participation in the EPAD-LCS (n = 413 (16%)) was associated with lower age (odds ratio (OR) age = 0.97 [0.95–0.99]), high education (OR = 1.64 [1.23–2.17]), male sex (OR = 1.56 [1.19–2.04]), and positive family history of dementia (OR = 1.66 [1.19–2.31]). Among participants in the EPAD-LCS, amyloid positivity (33%) was associated with higher age (OR = 1.06 [1.02–1.10]) and APOE ɛ4 allele carriership (OR = 2.99 [1.81–4.94]). These results were similar across prescreen settings. Conclusions Numbers needed to prescreen varied greatly between settings. Understanding how common AD risk factors link to study participation and amyloid positivity is informative for recruitment strategy of studies on secondary prevention of AD.

We describe a genome-wide analytical approach, SNP and Haplotype Regional Heritability Mapping (SNHap-RHM), that provides regional estimates of the heritability across locally defined regions in the genome. This approach utilises relationship matrices that are based on sharing of SNP and haplotype alleles at local haplotype blocks delimited by recombination boundaries in the genome. We implemented the approach on simulated data and show that the haplotype-based regional GRMs capture variation that is complementary to that captured by SNP-based regional GRMs, and thus justifying the fitting of the two GRMs jointly in a single analysis (SNHap-RHM). SNHap-RHM captures regions in the genome contributing to the phenotypic variation that existing genome-wide analysis methods may fail to capture. We further demonstrate that there are real benefits to be gained from this approach by applying it to real data from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study. We analysed height and major depressive disorder (MDD). We identified seven genomic regions that are genome-wide significant for height, and three regions significant at a suggestive threshold (p-value < 1 × 10−5) for MDD. These significant regions have genes mapped to within 400 kb of them. The genes mapped for height have been reported to be associated with height in humans. Similarly, those mapped for MDD have been reported to be associated with major depressive disorder and other psychiatry phenotypes. The results show that SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the “missing” heritability.

Background: Lung function is highly heritable and differs between the sexes throughout life. However, little is known about sex-differential genetic effects on lung function. We aimed to conduct the first genome-wide genotype-by-sex interaction study on lung function to identify genetic effects that differ between males and females. Methods: We tested for interactions between 7,745,864 variants and sex on spirometry-based measures of lung function in UK Biobank (N=303,612), and sought replication in 75,696 independent individuals from the SpiroMeta consortium. Results: Five independent single-nucleotide polymorphisms (SNPs) showed genome-wide significant (P

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

Background: Lung function is highly heritable and differs between the sexes throughout life. However, little is known about sex-differential genetic effects on lung function. We aimed to conduct the first genome-wide genotype-by-sex interaction study on lung function to identify genetic effects that differ between males and females. Methods: We tested for interactions between 7,745,864 variants and sex on spirometry-based measures of lung function in UK Biobank (N=303,612), and sought replication in 75,696 independent individuals from the SpiroMeta consortium. Results: Five independent single-nucleotide polymorphisms (SNPs) showed genome-wide significant (P

Background: Generation Scotland (GS) is a population and family-based study of genetic and environmental health determinants. Recruitment to the Scottish Family Health Study component of GS took place between 2006-2011. Participants were aged 18 or over and consented to genetic studies, linkage to health records and recontact. Several recontact exercises have been successfully conducted aimed at a) recruitment to embedded or partner studies and b) the collection of additional data. As the cohort matures in age, we were interested in surveying attitudes to potential new approaches to data collection and recruitment. Methods: A ten-question online survey was sent to those participants who provided an email address. Results: We report a high level of positive responses to encouraging relatives to participate, to remote data and sample collection and for research access to stored newborn dried blood spots. Conclusions: The majority of current and prospective GS participants are likely to respond positively to future requests for remote data and sample collection.

Abstract Background Whilst observational studies establish that lower plasma 25-hydroxyvitamin D (25-OHD) levels are associated with higher risk of colorectal cancer (CRC), establishing causality has proven challenging. Since vitamin D is modifiable, these observations have substantial clinical and public health implications. Indeed, many health agencies already recommend supplemental vitamin D. Here, we explore causality in a large Mendelian randomisation (MR) study using an improved genetic instrument for circulating 25-OHD. Methods We developed a weighted genetic score for circulating 25-OHD using six genetic variants that we recently reported to be associated with circulating 25-OHD in a large genome-wide association study (GWAS) meta-analysis. Using this score as instrumental variable in MR analyses, we sought to determine whether circulating 25-OHD is causally linked with CRC risk. We conducted MR analysis using individual-level data from 10,725 CRC cases and 30,794 controls (Scotland, UK Biobank and Croatia). We then applied estimates from meta-analysis of 11 GWAS of CRC risk (18,967 cases; 48,168 controls) in a summary statistics MR approach. Results The new genetic score for 25-OHD was strongly associated with measured plasma 25-OHD levels in 2821 healthy Scottish controls (P = 1.47 × 10− 11), improving upon previous genetic instruments (F-statistic 46.0 vs. 13.0). However, individual-level MR revealed no association between 25-OHD score and CRC risk (OR 1.03/unit log-transformed circulating 25-OHD, 95% CI 0.51–2.07, P = 0.93). Similarly, we found no evidence for a causal relationship between 25-OHD and CRC risk using summary statistics MR analysis (OR 0.91, 95% CI 0.69–1.19, P = 0.48). Conclusions Despite the scale of this study and employing an improved score capturing more of the genetic contribution to circulating 25-OHD, we found no evidence for a causal relationship between circulating 25-OHD and CRC risk. Although the magnitude of effect for vitamin D suggested by observational studies can confidently be excluded, smaller effects sizes and non-linear relationships remain plausible. Circulating vitamin D may be a CRC biomarker, but a causal effect on CRC risk remains unproven.

Health-related traits are known to vary geographically. Here, Amador and colleagues show that regional variation of obesity-related traits in a Scottish population is influenced more by lifestyle differences than it is by genetic differences.

Individual SNPs have small effects on anthropometric traits, yet the impact of CNVs has remained largely unknown. Here, Kutalik and co-workers perform a large-scale genome-wide meta-analysis of structural variation and find rare CNVs associated with height, weight and BMI with large effect sizes.

Background Generation Scotland is a family-based genetic epidemiology study of ~24,000 volunteers from ~7000 families recruited across Scotland with the capacity for follow-up through record linkage and re-contact. Broad consent was obtained for linkage to “medical records” for 98% of the cohort. This created a resource for investigation of the genetics of common conditions, available to researchers worldwide. Methods Participants completed a demographic, health and lifestyle questionnaire, provided samples, and underwent detailed clinical assessment. The samples and data collected form a resource with broad consent for research on the genetics of conditions of current and projected public health importance. This has become a longitudinal dataset by linkage to routine NHS hospital, maternity, lab tests, prescribing, dentistry, and mortality data. Results Researchers can use the linked datasets to find prevalent and incident disease cases, and healthy controls, in a stratified population. They can also do targeted recruitment of participants to new studies, including recall by genotype. We have established and validated EHR linkage, overcoming technical and governance issues in the process. Using consented data avoids some limitations of safe havens for analysis. Genome-wide association studies (GWAS) have been done on a wide range of quantitative traits and biomarker measurements. Generation Scotland is a contributor to major international consortia and has collaborated with Dementia Platforms UK and Health Data Research UK to make the resources more widely known. There have been over 300 research collaborations, and GS data has contributed to 200 publications, with more in the pipeline. Conclusions Generation Scotland has thoroughly tested the linkage process and is extending it to include primary care data and scanned images, with plans to collect more samples and data. The resources are available to academic and commercial researchers through a managed access process (www.generationscotland.org).

Introduction Heritability (proportion of trait variation attributable to genetic factors) is not a fixed property. It can vary across different social settings and environments. Exploration of gene-environment interaction has been limited by lack of large sample sizes. Biobanks linked to electronic health records pose a solution to this sample size problem. Objectives and Approach Social inequalities in periodontal health have been well documented in the dental scientific literature. However, gene-socioeconomic status interaction has yet to be examined. We identified 2,192 cases and 11,525 controls from linked electronic periodontal treatment records within the Generation Scotland: Scottish Family Health Study (GS: SFHS) (www.generationscotland.org). The measure of socioeconomic status used was the Scottish Index of Multiple Deprivation. The objective of this study was to investigate the gene-socioeconomic status interaction within this data. A reaction norm model was used to evaluate the presence of a gene-socioeconomic status interaction in the statistical software ASReml. Results We estimated the heritability of periodontal disease at 10.42% (95% confidence interval 5.97-14.88%). Socioeconomic status modified the heritability of periodontal disease. The heritability of was 13.37%, 0.14% and 11.70% in areas of high, moderate and low deprivation respectively; indicating the occurrence of a gene-socioeconomic status interaction with periodontal disease. These results indicate that socioeconomic status explains a large portion of genetic variation in periodontal disease risk. This information suggests that effective intervention and prevention programs for periodontal disease should involve socioeconomic aspects in their planning, implementations and evaluation. For instance, interventions targeted to reduce smoking in more deprived subjects with a genetic predisposition to periodontal disease could enhance the effect of health promotion strategies in reducing risk. Conclusion/Implications This study presents contemporary evidence in a large population based cohort that gene-socioeconomic interaction leads to the progression of periodontal disease. This information may lead to the development of better preventative strategies for clinical dentistry.

Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based genetic epidemiology study of ~24,000 volunteers from ~7000 families recruited across Scotland between 2006 and 2011 with the capacity for follow-up through record linkage and re-contact. Broad consent was obtained for linkage to “medical records” for 98% of the cohort. Participants completed a questionnaire, provided samples, and underwent clinical assessment. The samples and data collected form a resource with consent for research on the genetics of health, becoming a longitudinal dataset by linkage to routine NHS hospital, maternity, lab test, prescribing, dentistry and mortality data. Researchers can use the linked datasets to test research hypotheses on a stratified population and target recruitment to new studies. We have established and validated EHR linkage, overcoming technical and governance issues in the process. We plan to collaborate with UK Biobank, creating a combined cohort of over 50,000 people in Scotland, and using the SHARE register to obtain new research samples from routine NHS tests. We will extend linkage to include primary care data and scanned images in the next year. The resources are available to academic and commercial researchers through a managed access process.

ABSTRACT Background Electronic health records provides unprecedented opportunity for their re-use in genetic epidemiological research. However, electronic health records data from clinical settings, such as dental practices may be inaccurate or of insufficient granularity to be of use in research. In this study, we wish to determine the utility of National Health Service (NHS) electronic dental treatment records in genetic epidemiological research. Objective To estimate the heritability of periodontal disease using NHS electronic dental treatment records linked to health and non-health data within the Generation Scotland: Scottish Family Health Study (GS:SFHS). Approach We linked 852,355 NHS Scotland electronic dental treatment records from April 2000 to July 2015 to 20,626 participants within the GS:SFHS with pedigree, genomic, sociodemographic and clinical data. We then conducted a proof-of-principle genetic epidemiological analysis using periodontal (gum) disease treatment records. The data set analysed, consisted of 160,508 dental treatment records from 13,717 study participants; 3,387 of which were periodontal treatment records (from 2,192 study participants). We adjusted for the effects of previous treatment record, interval since last treatment, age, sex, treatment year, and treatment month, Scottish index of multiple deprivation, alcohol consumption, diabetes diagnosis, and smoking status in a linear model in the statistical software ASReml. We then calculated the mean risk of periodontal disease for each study participant based on residuals extracted from the aforementioned model. Genome-complex trait analysis (GCTA; with correction for population stratification) was used to estimate the pedigree and genomic based heritability of periodontal disease. Results We estimate the familial heritability of periodontal (gum) disease at 10.42% (95% confidence interval 5.97-14.88%). The genomic component did not contribute significantly to the heritability estimate. Conclusion we have demonstrated the usefulness of electronic dental treatment records in population based genetic epidemiological research .This study has also, to the best of our knowledge provided the first population based estimates of the genetic parameters for periodontal disease; confirming its familial nature. This invaluable and unique data resource will allow the acceleration of oral health research in Scotland and the exploration of research questions that could not be considered previously.

[This corrects the article DOI: 10.1371/journal.pgen.1005804.].

Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors.

The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ∼4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P

Stature is a classical and highly heritable complex trait, with 80%-90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder (χ(2) = 83.89, df = 1; p = 5.2 × 10(-20)). There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.

Alzheimer's disease patients have deficits in specific cognitive domains, and susceptibility genes for this disease may influence human cognition in nondemented individuals. To evaluate the role of Alzheimer's disease-linked genetic variation on cognition and normal cognitive ageing, we investigated two Scottish cohorts for which assessments in major cognitive domains are available: the Lothian Birth Cohort of 1921 and the Lothian Birth Cohort of 1936, consisting of 505 and 998 individuals, respectively. 158 SNPs from eleven genes were evaluated. Single SNP analyses did not reveal any statistical association after correction for multiple testing. One haplotype from TRAPPC6A was associated with nonverbal reasoning in both cohorts and combined data sets. This haplotype explains a small proportion of the phenotypic variability (1.8%). These findings warrant further investigation as biological modifiers of cognitive ageing.

BACKGROUND:Genetic and biological evidence supports a role for DISC1 across a spectrum of major mental illnesses, including schizophrenia and bipolar disorder. There is evidence for genetic interplay between variants in DISC1 and in biologically interacting loci in psychiatric illness. DISC1 also associates with normal variance in behavioral and brain imaging phenotypes. METHODOLOGY:Here, we analyze public domain datasets and demonstrate correlations between variants in the DISC1 pathway genes and levels of gene expression. Genetic variants of DISC1, NDE1, PDE4B and PDE4D regulate the expression of cytoskeletal, synaptogenic, neurodevelopmental and sensory perception proteins. Interestingly, these regulated genes include existing targets for drug development in depression and psychosis. CONCLUSIONS:Our systematic analysis provides further evidence for the relevance of the DISC1 pathway to major mental illness, identifies additional potential targets for therapeutic intervention and establishes a general strategy to mine public datasets for insights into disease pathways.

Abstract  Defects in the human ALS2 gene, which encodes the 1,657-amino-acid residue protein alsin, are linked to several related motor neuron diseases. We created a structural model for the N-terminal 690-residue region of alsin through comparative modelling based on regulator of chromosome condensation 1 (RCC1). We propose that this alsin region contains seven RCC1-like repeats in a seven-bladed beta-propeller structure. The propeller is formed by a double clasp arrangement containing two segments (residues 1–218 and residues 525–690). The 306-residue insert region, predicted to lie within blade 5 and to be largely disordered, is poorly conserved across species. Surface patches of evolutionary conservation probably indicate locations of binding sites. Both disease-causing missense mutations—Cys157Tyr and Gly540Glu—are buried in the propeller and likely to be structurally disruptive. This study aids design of experimental studies by highlighting the importance of construct length, will enhance interpretation of protein–protein interactions, and enable rational site-directed mutagenesis. [ABSTRACT FROM AUTHOR]